Please use this identifier to cite or link to this item:
https://idr.l3.nitk.ac.in/jspui/handle/123456789/14893
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Akshara P. | |
dc.contributor.author | Shidharth S. | |
dc.contributor.author | Krishnan G.S. | |
dc.contributor.author | Sowmya Kamath S. | |
dc.date.accessioned | 2021-05-05T10:15:57Z | - |
dc.date.available | 2021-05-05T10:15:57Z | - |
dc.date.issued | 2020 | |
dc.identifier.citation | ACM International Conference Proceeding Series , Vol. , , p. 436 - | en_US |
dc.identifier.uri | https://doi.org/10.1145/3430984.3431060 | |
dc.identifier.uri | http://idr.nitk.ac.in/jspui/handle/123456789/14893 | - |
dc.description.abstract | The large-scale availability of healthcare data provides significant opportunities for development of advanced Clinical Decision Support Systems that can enhance patient care. One such essential application is automated ICD-9 diagnosis group prediction, useful for a variety of healthcare delivery related tasks including documenting, billing and insurance claims. Past attempts considered patients' multivariate lab events data and clinical text notes independently. To the best of our knowledge, ours is the first attempt to investigate the efficacy of integration of both these aspects for this task. Experiments on MIMIC-III dataset showed promising results. © 2021 Owner/Author. | en_US |
dc.title | Integrating Structured and Unstructured Patient Data for ICD9 Disease Code Group Prediction | en_US |
dc.type | Conference Paper | en_US |
Appears in Collections: | 2. Conference Papers |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.